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Unsupervised acquisition of entailment relations from the Web

Published online by Cambridge University Press:  30 July 2013

IDAN SZPEKTOR ,
HRISTO TANEV ,
IDO DAGAN ,
BONAVENTURA COPPOLA  and
MILEN KOUYLEKOV
IDAN SZPEKTOR
Affiliation:
Yahoo! Research, Haifa, Israel e-mail: [email protected]
HRISTO TANEV
Affiliation:
JRC, Ispra, Italy e-mail: [email protected]
IDO DAGAN
Affiliation:
Department of Computer Science, Bar-Ilan University, Ramat Gan, Israel e-mail: [email protected]
BONAVENTURA COPPOLA
Affiliation:
IBM Thomas J. Watson Research Center, Yorktown Heights, NY e-mail: [email protected]
MILEN KOUYLEKOV
Affiliation:
CELI s.r.l., Torino, Italy e-mail: [email protected]
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Abstract

Entailment recognition is a primary generic task in natural language inference, whose focus is to detect whether the meaning of one expression can be inferred from the meaning of the other. Accordingly, many NLP applications would benefit from high coverage knowledgebases of paraphrases and entailment rules. To this end, learning such knowledgebases from the Web is especially appealing due to its huge size as well as its highly heterogeneous content, allowing for a more scalable rule extraction of various domains. However, the scalability of state-of-the-art entailment rule acquisition approaches from the Web is still limited. We present a fully unsupervised learning algorithm for Web-based extraction of entailment relations. We focus on increased scalability and generality with respect to prior work, with the potential of a large-scale Web-based knowledgebase. Our algorithm takes as its input a lexical–syntactic template and searches the Web for syntactic templates that participate in an entailment relation with the input template. Experiments show promising results, achieving performance similar to a state-of-the-art unsupervised algorithm, operating over an offline corpus, but with the benefit of learning rules for different domains with no additional effort.

Type
Articles
Information
Natural Language Engineering , Volume 21 , Issue 1 , January 2015 , pp. 3 - 47
Copyright
Copyright © Cambridge University Press 2013 

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